1 Creating an R object from Spectronaut output files

[Skip for now]

filetable <-  read.table(file="/Users/shubhamagrawal/Documents/work/compressed/DIA/fileTable.txt", header = TRUE)

mae <- readExperimentDIA(fileTable = filetable, 
                         annotation_col = c("treatment", "timepoint", "replicate",
                                            "sampleType"))

2 Use the created object

mae <- readRDS("data/maeObjNEW.Rds")
mae
## A MultiAssayExperiment object of 2 listed
##  experiments with user-defined names and respective classes.
##  Containing an ExperimentList class object of length 2:
##  [1] Phosphoproteome: SummarizedExperiment with 40517 rows and 138 columns
##  [2] Proteome: SummarizedExperiment with 8518 rows and 79 columns
## Functionality:
##  experiments() - obtain the ExperimentList instance
##  colData() - the primary/phenotype DataFrame
##  sampleMap() - the sample coordination DataFrame
##  `$`, `[`, `[[` - extract colData columns, subset, or experiment
##  *Format() - convert into a long or wide DataFrame
##  assays() - convert ExperimentList to a SimpleList of matrices
##  exportClass() - save data to flat files

3 Preprocessing the assay, basic visualization, PCA

se <- mae[["Phosphoproteome"]]
colData(se) <- colData(mae[, colnames(se)])
se
## class: SummarizedExperiment 
## dim: 40517 138 
## metadata(0):
## assays(1): Intensity
## rownames(40517): s1 s2 ... s40516 s40517
## rowData names(6): UniprotID Gene ... Sequence site
## colnames(138): FullProteome_1stCtrl_0min_rep2
##   FullProteome_1stCtrl_0min_rep3 ... Phospho_HGF_40min_rep1
##   Phospho_HGF_6h_rep1
## colData names(6): treatment timepoint ... sample sampleName
plotIntensity(se[, se$sampleType == "Phospho"], color = "replicate") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0, size = 7),
    plot.title = element_text(hjust = 0.5, face = "bold")
  ) 

newSE <- preprocessPhos(seData = se, transform = "log2", 
                        normalize = TRUE, impute = "QRILC")
## Warning in fun(libname, pkgname): mzR has been built against a different Rcpp version (1.0.12)
## than is installed on your system (1.1.0). This might lead to errors
## when loading mzR. If you encounter such issues, please send a report,
## including the output of sessionInfo() to the Bioc support forum at 
## https://support.bioconductor.org/. For details see also
## https://github.com/sneumann/mzR/wiki/mzR-Rcpp-compiler-linker-issue.
## Imputing along margin 2 (samples/columns).
newSE
## class: SummarizedExperiment 
## dim: 13081 59 
## metadata(0):
## assays(2): Intensity imputed
## rownames(13081): s1 s4 ... s40511 s40514
## rowData names(6): UniprotID Gene ... Sequence site
## colnames(59): Phospho_1stCtrl_0min_rep2 Phospho_1stCtrl_0min_rep3 ...
##   Phospho_HGF_40min_rep1 Phospho_HGF_6h_rep1
## colData names(6): treatment timepoint ... sample sampleName
plotIntensity(newSE, color = "replicate") + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0, size = 7),
    plot.title = element_text(hjust = 0.5, face = "bold")
  ) 

plotMissing(newSE) + theme_classic() +
  theme(
    axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0, size = 7),
    plot.title = element_text(hjust = 0.5, face = "bold")
  ) 

4 Perform PCA

pca <- stats::prcomp(t(assays(newSE)[["imputed"]]), center = TRUE, scale. = TRUE)
p <- plotPCA(pca = pca, se = newSE,
             color = "treatment",
             shape = "replicate")

p$layers[[1]]$aes_params$size   <- 3
p$layers[[1]]$aes_params$stroke <- 1.2

p + coord_equal() + theme(aspect.ratio = 1, legend.position = "right",
                          axis.text = element_text(color = "#000000")) +
  guides(
    shape = guide_legend(order = 1),
    color = guide_legend(order = 2)
    )

5 rep1 samples need to be removed or perform batch correction

newSE <- preprocessPhos(seData = se, transform = "log2", 
                        normalize = TRUE, impute = "QRILC", 
                        removeOutlier = "rep1")
## Imputing along margin 2 (samples/columns).
newSE
## class: SummarizedExperiment 
## dim: 17035 32 
## metadata(0):
## assays(2): Intensity imputed
## rownames(17035): s1 s4 ... s40513 s40514
## rowData names(6): UniprotID Gene ... Sequence site
## colnames(32): Phospho_1stCtrl_0min_rep2 Phospho_1stCtrl_0min_rep3 ...
##   Phospho_HGF_40min_rep2 Phospho_HGF_40min_rep3
## colData names(6): treatment timepoint ... sample sampleName
pca <- stats::prcomp(t(assays(newSE)[["imputed"]]), 
                     center = TRUE, scale. = TRUE)
p <- plotPCA(pca = pca, se = newSE,
             color = "treatment",
             shape = "replicate")

p$layers[[1]]$aes_params$size   <- 3
p$layers[[1]]$aes_params$stroke <- 1.2

p + coord_equal() + theme(aspect.ratio = 1, legend.position = "right",
                          axis.text = element_text(color = "#000000")) +
  guides(
    shape = guide_legend(order = 1),
    color = guide_legend(order = 2)
    )

p <- plotHeatmap(type = "Top variant", newSE, top = 30, annotationCol = c("replicate", "treatment")) 

g <- p$gtable

# Find row names grob
row_names <- which(g$layout$name == "row_names")
g$grobs[[row_names]]$gp <- gpar(fontsize = 8, fontface = "bold")
# Same for columns
col_names <- which(g$layout$name == "col_names")
g$grobs[[col_names]]$gp <- gpar(fontsize = 10)

grid.newpage()
grid.draw(g)

6 Differential expression

dea <- performDifferentialExp(se = newSE, 
                              assay = "imputed", 
                              method = "limma", 
                              condition = "treatment", 
                              reference = "2ndCtrl", 
                              target = "HGF")
p <- ggplot(dea$resDE, aes(x = pvalue)) +
  geom_histogram(fill = "grey", col = "blue", alpha=0.7) +
  ggtitle("P value histogram") + theme_classic() +
  theme(plot.title = element_text(hjust = 0.5)) 
  
p
## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.

pFilter = 0.01
plotVolcano(dea$resDE, pFilter = 0.01, fcFilter = 1) + theme_classic() +
  geom_hline(yintercept = -log10(as.numeric(pFilter)), color = "brown",
               linetype = "dashed") +
    annotate(x = 3.0, y = -log10(as.numeric(pFilter)) - 0.20,
             label = paste0("P-value = ", as.numeric(pFilter)),
             geom = "text", size = 3.5, color = "brown") 

plotVolcanoDEA(dea$resDE, fcFilter = 1, pFilter = 0.01, usePadj = FALSE)

dea$resDE
## # A tibble: 17,035 × 11
##    ID    log2FC  stat   pvalue    padj UniprotID Gene  Position Residue Sequence
##    <I<c>  <dbl> <dbl>    <dbl>   <dbl> <chr>     <chr> <chr>    <chr>   <chr>   
##  1 s129…   3.41  27.9 1.47e-11 1.79e-7 Q07889    SOS1  1134     S       PHGPRSA…
##  2 s337…   2.28  27.0 2.10e-11 1.79e-7 Q9H8W4    PLEK… 226      S       CQPARSD…
##  3 s360…   5.51  23.9 7.91e-11 4.49e-7 Q9P246    STIM2 697      S       VPKPRHT…
##  4 s356…   2.17  23.1 1.14e-10 4.86e-7 Q9NZN5    ARHG… 341      S       DTQSLVG…
##  5 s7013   2.36  20.7 3.67e-10 1.25e-6 P19634    SLC9… 703      S       MSRARIG…
##  6 s110…   1.40  19.7 6.20e-10 1.76e-6 P55196    AFDN  1799     S       KERQRLF…
##  7 s315…   2.80  18.7 1.07e- 9 2.35e-6 Q9BXB5    OSBP… 209      S       PCSQRHL…
##  8 s335…   5.35  18.7 1.10e- 9 2.35e-6 Q9H7D0    DOCK5 1794     T       PLSPPPL…
##  9 s3430   5.99  17.5 2.18e- 9 3.88e-6 O75427    LRCH4 521      S       QSGSGPS…
## 10 s129…   2.37  17.4 2.32e- 9 3.88e-6 Q07889    SOS1  1229     S       TPDVFSS…
## # ℹ 17,025 more rows
## # ℹ 1 more variable: site <chr>
intensityBoxPlot(se = dea$seSub, id = 's4971', symbol = "EGFR_S991")

7 Time series clustering

set.seed(12345)

newSEts <- newSE[ , newSE$treatment == "HGF"]
assayMat <- SummarizedExperiment::assay(newSEts)

exprMat <- lapply(unique(newSEts$timepoint), function(tp) {
  rowMedians(assayMat[,newSEts$timepoint == tp])
  }) %>% bind_cols() %>% as.matrix()
## New names:
## • `` -> `...1`
## • `` -> `...2`
## • `` -> `...3`
## • `` -> `...4`
rownames(exprMat) <- rownames(newSEts)
colnames(exprMat) <- unique(newSEts$timepoint)

sds <- apply(exprMat,1,sd)
varPer <- 80
exprMat <- exprMat[order(sds, decreasing = TRUE)[seq(1, varPer/100*nrow(exprMat))], ]
# only center when it's for expression
exprMat <- mscale(exprMat)
# remove NA values
exprMat <- exprMat[complete.cases(exprMat), ]


tsc <- clusterTS(x = exprMat, k = 5, pCut = 0.6)
tsc$plot + theme(
  axis.text.x = element_text(size = 10), axis.text = element_text(color = "#000000"), ) 

8 Enrichment analysis

genesetPath <- system.file("shiny-app/geneset", package = "SmartPhos")
inGMT1 <- piano::loadGSC(paste0(genesetPath, "/Cancer_Hallmark.gmt"), 
                         type="gmt")
resTab <- enrichDifferential(dea = dea$resDE, type = "Pathway enrichment", 
                             gsaMethod = "PAGE", geneSet = inGMT1, 
                             statType = "stat", nPerm = 200, sigLevel = 0.05, 
                             ifFDR = FALSE)
## Running gene set analysis:
## Checking arguments...done!
## Final gene/gene-set association: 1218 genes and 50 gene sets
##   Details:
##   Calculating gene set statistics from 1218 out of 4183 gene-level statistics
##   Removed 3168 genes from GSC due to lack of matching gene statistics
##   Removed 0 gene sets containing no genes after gene removal
##   Removed additionally 0 gene sets not matching the size limits
##   Loaded additional information for 50 gene sets
## Calculating gene set statistics...done!
## Calculating gene set significance...done!
## Adjusting for multiple testing...done!
resTab
##                                Name Gene Number    Stat       p.up p.up.adj
## 1          HALLMARK_MITOTIC_SPINDLE         145  3.2903 0.00050045 0.025022
## 2  HALLMARK_PI3K_AKT_MTOR_SIGNALING          45  2.6387 0.00416070 0.104020
## 3      HALLMARK_ALLOGRAFT_REJECTION          28  2.4104 0.00796780 0.132800
## 4  HALLMARK_IL6_JAK_STAT3_SIGNALING          16  2.0783 0.01884200 0.235530
## 5           HALLMARK_APICAL_SURFACE          10  1.8437 0.03261600 0.306600
## 6           HALLMARK_UV_RESPONSE_DN          53  1.7892 0.03679200 0.306600
## 7      HALLMARK_PANCREAS_BETA_CELLS           2  1.6815 0.04632900 0.330920
## 8       HALLMARK_HEDGEHOG_SIGNALING          10 -2.4368 0.99259000 0.999920
## 9               HALLMARK_DNA_REPAIR          45 -2.5636 0.99482000 0.999920
## 10          HALLMARK_G2M_CHECKPOINT         116 -2.8153 0.99756000 0.999920
## 11          HALLMARK_MYC_TARGETS_V1         112 -3.1157 0.99908000 0.999920
## 12             HALLMARK_E2F_TARGETS         116 -3.7673 0.99992000 0.999920
##        p.down p.down.adj Number up Number down
## 1  9.9950e-01  0.9995000        76          69
## 2  9.9584e-01  0.9995000        25          20
## 3  9.9203e-01  0.9995000        15          13
## 4  9.8116e-01  0.9995000        10           6
## 5  9.6738e-01  0.9995000         7           3
## 6  9.6321e-01  0.9995000        30          23
## 7  9.5367e-01  0.9995000         1           1
## 8  7.4090e-03  0.0740900         1           9
## 9  5.1793e-03  0.0647410        14          31
## 10 2.4367e-03  0.0406110        35          81
## 11 9.1769e-04  0.0229420        38          74
## 12 8.2504e-05  0.0041252        33          83
inGMT2 <- piano::loadGSC(paste0(genesetPath, "/KEGG_pathways.gmt"), 
                         type="gmt")
clustEnr <- clusterEnrich(clusterTab = tsc$cluster, se = newSE, 
                          inputSet = inGMT2, filterP = 0.01, ifFDR = FALSE)
clustEnr$plot + theme_classic()

9 Kinase activity inference

First on the differential expression analysis results

netw <- getDecouplerNetwork("Homo sapiens")
scoreTab <- calcKinaseScore(dea$resDE, netw, statType = "stat", nPerm = 100)
plotKinaseDE(scoreTab, nTop = 10, pCut = 0.05)

clusterData <- tsc$cluster[tsc$cluster$cluster == "cluster1",]
allClusterFeature <- clusterData %>%
  distinct(feature, .keep_all = TRUE) %>% .$feature
allClusterSite <- data.frame(rowData(newSEts)[allClusterFeature, "site"])
allClusterSite$feature <- allClusterFeature
clusterData <- clusterData %>%
  left_join(allClusterSite, by = "feature") %>%
  rename(site = "rowData.newSEts..allClusterFeature...site..")
scoreTab <- calcKinaseScore(clusterData, netw, statType = "stat", nPerm = 100)
plotKinaseTimeSeries(scoreTab, pCut = 0.05, clusterName = "cluster1")

10 Phosphosites pattern

newSEts <- addZeroTime(newSE, condition = "treatment", treat = "HGF", 
                       zeroTreat = "2ndCtrl", 
                       timeRange = c("20min","40min","100min"))
rd <- as.data.frame(rowData(newSEts))
timerange <- unique(newSEts$timepoint)
sites <-  c("MET_Y1234",  "MET_Y1235", "MET_1003", "Shc1_Y427", "AKT3_Y312", "RPS6KB1", "EGFR_Y1092", "EGFR_Y1172", "SOS1_S1134", "MAP2K2_S226", "MAPK3_T202", "MAPK1_T190", "EGFR_Y1197", "ERBB2_S998","GAB1_Y659","RPS6_S244")

for (i in sites) {
  # condition to check if that site is present in row data
  if (i %in% rd$site) {
    # condition to check if assay doesn't have NAs (imputed assays are not allowed)
    if (!is.na(assay(newSEts)[rownames(rd[rd$site==i,]), ][1])) {
      p <- plotTimeSeries(newSEts, type = "expression", 
                        geneID = rownames(rd[rd$site==i,]), 
                        symbol = i, condition = "treatment", 
                        treatment = "HGF", timerange = timerange) +
        theme(axis.text = element_text(color = "#000000", size = 20), 
              axis.text.x = element_text(angle = 0, vjust = 0, hjust = 0.5, size = 20),
              axis.title = element_text(size=20),
              plot.title = element_text(size=30)) +  
        scale_x_continuous(breaks = c(0,20,40,60,80,100))
      p$layers[[1]]$aes_params$size <- 6
      p$layers[[1]]$mapping$colour <- NULL
      p$layers[[1]]$aes_params$colour <- "#c1191f"
      p$layers[[2]]$aes_params$linewidth <- 1
      p$layers[[2]]$mapping$colour <- NULL
      p$layers[[2]]$aes_params$colour <- "#555555"
      print(p)
    }
  }
}